TY - GEN
T1 - Sparse hidden Markov models for purer clusters
AU - Bharadwaj, Sujeeth
AU - Hasegawa-Johnson, Mark
AU - Ajmera, Jitendra
AU - Deshmukh, Om
AU - Verma, Ashish
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013/10/18
Y1 - 2013/10/18
N2 - The hidden Markov model (HMM) is widely popular as the de facto tool for representing temporal data; in this paper, we add to its utility in the sequence clustering domain - we describe a novel approach that allows us to directly control purity in HMM-based clustering algorithms. We show that encouraging sparsity in the observation probabilities increases cluster purity and derive an algorithm based on lp regularization; as a corollary, we also provide a different and useful interpretation of the value of p in Renyi p-entropy. We test our method on the problem of clustering non-speech audio events from the BBC sound effects corpus. Experimental results confirm that our approach does learn purer clusters, with (unweighted) average purity as high as 0.88 - a considerable improvement over both the baseline HMM (0.72) and k-means clustering (0.69).
AB - The hidden Markov model (HMM) is widely popular as the de facto tool for representing temporal data; in this paper, we add to its utility in the sequence clustering domain - we describe a novel approach that allows us to directly control purity in HMM-based clustering algorithms. We show that encouraging sparsity in the observation probabilities increases cluster purity and derive an algorithm based on lp regularization; as a corollary, we also provide a different and useful interpretation of the value of p in Renyi p-entropy. We test our method on the problem of clustering non-speech audio events from the BBC sound effects corpus. Experimental results confirm that our approach does learn purer clusters, with (unweighted) average purity as high as 0.88 - a considerable improvement over both the baseline HMM (0.72) and k-means clustering (0.69).
KW - Renyi entropy
KW - cluster purity
KW - hidden Markov model
KW - sequence clustering
KW - sparsity
UR - http://www.scopus.com/inward/record.url?scp=84890532859&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890532859&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6638228
DO - 10.1109/ICASSP.2013.6638228
M3 - Conference contribution
AN - SCOPUS:84890532859
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3098
EP - 3102
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -